计算机科学
粒度
分类器(UML)
隐马尔可夫模型
人工智能
多样性(控制论)
语音识别
模式识别(心理学)
机器学习
操作系统
作者
Ling Ma,Ben Milner,Dan Smith
出处
期刊:ACM Transactions on Speech and Language Processing
[Association for Computing Machinery]
日期:2006-07-01
卷期号:3 (2): 1-22
被引量:131
标识
DOI:10.1145/1149290.1149292
摘要
The acoustic environment provides a rich source of information on the types of activity, communication modes, and people involved in many situations. It can be accurately classified using recordings from microphones commonly found in PDAs and other consumer devices. We describe a prototype HMM-based acoustic environment classifier incorporating an adaptive learning mechanism and a hierarchical classification model. Experimental results show that we can accurately classify a wide variety of everyday environments. We also show good results classifying single sounds, although classification accuracy is influenced by the granularity of the classification.
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